Prediction of wind-induced pressure on pentagon plan shape building using artificial neural network
By: Vigneshwaran, R
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Contributor(s): Prabavathy, S.
Publisher: New York Springer 2022Edition: Vol.103(2), June.Description: 581-600p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series ASummary: Nowadays, with the development of composite materials and construction technique, it is feasible to construct tall buildings to a desired height. In the design of tall buildings, wind loads are the major design criteria to be considered by structural engineers and architects. This paper explores the mean pressure coefficient (mean Cp) on pentagon plan shape building using artificial neural networks (ANN). The input for ANN is obtained by performing CFD simulation for the wind angles 0° to 180° at an interval of 15°. The Levenberg–Marquardt training function and mean square error (MSE) performance function are utilized to train the target data. The network is trained till the correlation (R) reaches between 0.9 and 1, respectively. The results have shown that the mean values of Cp obtained from CFD and ANN are in good agreement. Furthermore, mean pressure coefficient (Cp) for intermediate wind angles for pentagon plan shape building is obtained using ANN. A sample check is made on the wind angles 35° and 165°, and the obtained results are within the permissible limits.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-1756 |
Nowadays, with the development of composite materials and construction technique, it is feasible to construct tall buildings to a desired height. In the design of tall buildings, wind loads are the major design criteria to be considered by structural engineers and architects. This paper explores the mean pressure coefficient (mean Cp) on pentagon plan shape building using artificial neural networks (ANN). The input for ANN is obtained by performing CFD simulation for the wind angles 0° to 180° at an interval of 15°. The Levenberg–Marquardt training function and mean square error (MSE) performance function are utilized to train the target data. The network is trained till the correlation (R) reaches between 0.9 and 1, respectively. The results have shown that the mean values of Cp obtained from CFD and ANN are in good agreement. Furthermore, mean pressure coefficient (Cp) for intermediate wind angles for pentagon plan shape building is obtained using ANN. A sample check is made on the wind angles 35° and 165°, and the obtained results are within the permissible limits.
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